The ARCHIVE Storage Engine

I wonder how much longer the ARCHIVE storage engine is going to ship with MySQL…. I think I’m the last person to actually fix a bug in it, and that was, well, a good number of years ago now. It was created to solve a simple problem: write once read hardly ever. Useful for logs and the like. A zlib stream of rows in a file.

You can actually easily beat ARCHIVE for INSERT speed with a non-indexed MyISAM table, and with things like TokuDB around you can probably get pretty close to compression while at the same time having these things known as “indexes”.

ARCHIVE for a long time held this niche though and was widely and quietly used (and likely still is). It has the great benefit of being fairly lightweight – it’s only about 2500 lines of code (1130 if you exclude azio.c, the slightly modified gzio.c from zlib).

It also use the table discovery mechanism that NDB uses. If you remove the FRM file for an ARCHIVE table, the ARCHIVE storage engine will extract the copy it keeps to replace it. You can also do consistent backups with ARCHIVE as it’s an append-only engine. The ARCHIVE engine was certainly the simplest example code of this and a few other storage engine API things.

I’d love to see someone compare storage space and performance of ARCHIVE against TokuDB and InnoDB (hint hint, the Internet should solve this for me).

Where are they now: MySQL Storage Engines

There was once a big hooplah about the MySQL Storage Engine Architecture and how it was easy to just slot in some other method of storage instead of the provided ones. Over the years I’ve repeatedly mentioned how this wasn’t really the case and that it was remarkably non trivial.

Over the years there have been many storage engines crop up and then disappear. So… where are they now?

  • ISAM
    This became MyISAM…. you know you’ve been around MySQL a long time if you’ve ever had to deal with an ISAM table.
  • Gemini
    This was the first big test of the GPL in court. Basically, you have to obey the GPL (see wikipedia for more info). The code was released as GPL and development stopped. This has been dead since ca 2002.
  • Amira – http://launchpad.net/amira
    Antony first mentioned this in 2008 on his blog. This was a continuation of the Gemini engine, you can actually go over to launchpad and get the code. This was one of the projects to have a transactional storage engine not owned by Oracle after Innobase Oy was acquired by them. It went nowhere special as Netfrastructure was acquired which became Falcon.
  • BDB
    otherwise known as the BerkeleyDB engine. It was seldom used and never gained much of a userbase. It was unceremoniously dropped back in 2006 and both users didn’t really exist.
  • PBXT – http://pbxt.blogspot.com/
    I think we can credit PBXT with at least half of the features and performance improvements to InnoDB since it first emerged back in 2006. It got attention very quickly. Why? Because it was different. It had the very rare ability to outperform InnoDB in some places. You can still find PBXT in MariaDB, but sadly it can be hard to fund development of a MySQL storage engine, especially one as tied to MySQL as PBXT is, and it’s no longer under active development. Closely related was the Blob Streaming project which was way ahead of its time as an AlsoSQL access method. The good news is that the code was released under a BSD license in 2012 (was previously GPL). We even had PBXT in Drizzle for a while.
  • Blob Streaming (PBMS) – http://bpbdev.blogspot.com/
    This project was closely related to (but not depending exclusively on) PBXT. It embedded a HTTP server inside the database and could use it to read and write BLOBs. This was not only fairly cool but way ahead of its time. We owe the existence of both HandlerSocket and the memcached interface to InnoDB to PBMS (it was also an inspiration for the JSON server plugin for Drizzle, to address some of the use cases of the PBMS plugin).
  • Federated
    It’s still there… but is effectively unmaintained and dead. There’s even FederatedX in MariaDB which is an improvement, but still, the MySQL server really doesn’t lend itself kindly to this type of engine… it’s always been an oddity only suitable for very specific tasks.
  • Archive
    Although useful, effectively unmaintained. I kinda don’t want to say dead… but if it went away, I wouldn’t exactly be surprised.
  • CSV
    Currently used to access the log tables in MySQL… and hardly used otherwise. It’s odd that the same code doesn’t deal with SELECT INTO OUTFILE and LOAD DATA INFILE, and I doubt this will ever change. I’d say effectively niche/dead.
  • SolidDB
    Purchased by IBM, abandoned.
  • DB2
    Only ever on System i. Useful for very very few people… but you can still find it around if you’re one of them.
  • Infobright
    OMG it exists! This is probably because they’re largely just using the MySQL server as a way to implement the MySQL network protocol and all of the heavy lifting is done by their own code.
  • Xeround
    I’m quite surprised these guys are still around, as they’re a proprietary storage engine as a service, and initial testing wasn’t entirely promising.
  • TokuDB
    I cannot emphasize how much more interesting TokuDB would be if it were open source. It actually holds some promise… and with their recent work with mongo, perhaps this is a good way forward for them…
  • Maria/Aria
    Another “OMG Oracle just bought Innobase Oy” engine. This was a project to take MyISAM and turn it into a lean, mean, transactional storage engine machine. It’s still not there and I don’t think it ever will be.
  • Falcon
    This was the hot new thing. It came out of Netfrastructure, which MySQL AB acquired in order to help get a transactional storage engine after Innobase Oy was acquired by Oracle. If you’re keeping count, that’s three projects for a transactional storage engine. Falcon was the star though, receiving all the press and publicity (well before it was ready). There are many reasons why Falcon isn’t around today – the chief one probably being that Oracle bought Sun who had bought MySQL and thus a need for an “InnoDB replacement” instantly vanished. There was also immense management pressure for performance to be greater than InnoDB, without any allowance for or focus on correctness…. and this showed. This was quite disappointing as Falcon had a lot of good architectural things going for it.
  • BlitzDB – https://launchpad.net/blitzdb
    I had hoped we’d replace MyISAM with BlitzDB in Drizzle. It was a wrapper around Tokyo Cabinet to the storage engine API in Drizzle. Unfortunately, the ties to MyISAM are incredibly deep (see my recent post on internal temporary tables) and we never quite got there.

I think this is all the notable engines that were aimed at widespread adoption… what ones have I forgotten?

It’s interesting to note that only Archive, CSV, Xeround, TokuDB and Infobright can be gotten anywhere, and the latter two only in their own distribution (one proprietary) and Xeround only as a service.

A tale of a bug…

So I sometimes get asked if we funnel back bug reports or patches back to MySQL from Drizzle. Also, MariaDB adds some interest here as they are a lot closer (and indeed compatible with) to MySQL. With Drizzle, we have deviated really quite heavily from the MySQL codebase. There are still some common areas, but they’re getting rarer (especially to just directly apply a patch).

Back in June 2009, while working on Drizzle at Sun, I found a bug that I knew would affect both. The patch would even directly apply (well… close, but I made one anyway).

So the typical process of me filing a MySQL bug these days is:

  • Stewart files bug
  • In the next window of Sveta being awake, it’s verified.

This happened within a really short time.

Unfortunately, what happens next isn’t nearly as awesome.

Namely, nothing. For a year.

So a year later, I filed it in launchpad for MariaDB.

So, MariaDB is gearing up for a release, it’s a relatively low priority bug (but it does have a working, correct and obvious patch), within 2 months, Monty applied it and improved the error checking around it.

So MariaDB bug 588599 is Fix Committed (June 2nd 2010 – July 20th 2010), MySQL Bug 45377 is still Verified (July 20th 2009 – ….).

(and yes, this tends to be a general pattern I find)

But Mark says he gets things through… so yay for him.2

Drizzle FRM replacement: the table proto

Drizzle originally inherited the FRM file from MySQL (which inherited it from UNIREG). The FRM file stores metadata about a table; what columns it has, what type those columns are, what indexes, any default values, comments etc are all stored in the FRM. In the days of MyISAM, this worked relatively well. The row data was stored in table.MYD, indexes on top of it in table.MYI and information about the format of the row was
in table.FRM. Since MyISAM itself wasn’t crash safe, it didn’t really matter if creating/deleting the FRM file along with the table was either.

As more sophisticated engines were introduced (e.g. InnoDB) that had their own data dictionary, there started to be more of a problem. There were now two places storing information about a table: the FRM file and the data dictionary specific to the engine. Even if the data dictionary of the storage engine was crash safe, the FRM file was not plugged into that, so you could end up in a situation where the storage engine
recovered from a crash okay, but the FRM was incorrect for what the engine recovered to. This would always require manual intervention to find out what went wrong and then fix it (in some rather unusual ways).

When the MySQL Cluster (NDB) engine was introduced, a new set of problems arose. Now the MySQL server was connecting to an existing database, where tables could be created on other nodes connected to the cluster. You now not only had the problems of crash recovery, but the problems of keeping the FRM files in sync across many nodes, requiring
all sorts of interesting solutions that, for the most part, do work.

The “obvious” solution to some of these problems would be for an engine to write out an FRM file itself. This is much easier said than done. The file format was never created to be read and written by multiple pieces of software, the code that did the reading and writing inside the server was not reusable elsewhere and the only documentation (that
wasn’t a decent chunk of the MySQL source tree) is the rather incomplete definition in the MySQL Internals wiki (http://forge.mysql.com/wiki/MySQL_Internals_File_Formats) – not nearly enough to write a correct FRM file as the specifics are very, very odd.

Our goals for reworking the metadata system in Drizzle were: to allow engines to own their own metadata (removing any opportunity to have inconsistencies between the engine and the ‘FRM’) and for engines without their own data dictionary, to replace the FRM file format with something simple and well documented.

One option was to use SQL as the standard storage format, but it is rather non-trivial and expensive to parse – especially if we were to use it as the preferred way of talking table definitions with storage engines. We had been looking at the protobuf library
(http://code.google.com/p/protobuf/) ever since its first release and it has a number of very nice characteristics: a description language of a data structure that is then used to generate APIs for reading and writing it in a number of programming languages and a standard (documented) way to serialize the data structure.

After a bit of discussion, we arrived at a good outline for the table definition proto. The current one can always be found in the Drizzle source tree at drizzled/message/table.proto. The current format is very close to final (i.e. one that we’ll suppport upgrades from).

The process of modifying the Drizzle code base so that it would write (and read) a file format different to the FRM isn’t worth going too much into here although there were some interesting hurdles to overcome. An interesting one was the FRM file contains a binary image of the default row for the table (which is in the row format that the server uses); we now store the default value for each column in the proto and generate the default row when we read the proto. Another interesting one was removing and refactoring “pack_flag” – the details of which should only be extracted from Jay or Stewart with a liberal application of fine ale.

The end result is that we now have storage engines that are completely responsible for their own metadata. One example is the ARCHIVE engine. In the CREATE TABLE code path, the ARCHIVE storage engine gets the table definition in an object that represents the table proto. It can examine the parameters it needs to and then either store the proto directly, or convert it into its own format. Since ARCHIVE is simple, it just stores
the table proto in a serialised form (using a standard function provided by the protobuf library) and stores it in the .ARZ data file for the table. This instantly makes the ARCHIVE storage engine crash safe for CREATE and DROP table as there is only 1 file on disk, so no two files to get out of sync.

If an engine does not have its own data dictionary, it can still use the default implementation which just stores the serialised table proto in a file on disk.

We can also now use this interface to move INFORMATION_SCHEMA into its own storage engine. This means we can remove a lot of special case code throughout the server for INFORMATION_SCHEMA and instead just have a INFORMATION_SCHEMA storage engine that says it has the following tables in the INFORMATION_SCHEMA database. Because the table definition is now in a documented format with a standard API, this becomes a relatively trivial exercise.

What we’re all looking forward to is when the InnoDB data dictionary is linked into the new interface and we can have a truly crash safe database server.

Another wonderful side effect is since we now have a standard data structure for representing a table definition, we can integrate this with the replication system. In the “near” future, we can represent a CREATE TABLE in the replication stream as a table proto and not the raw SQL. If you were wanting to apply the replication stream to a different database server, you then only have to write a table proto to SQL
converter. If the target database system doesn’t do SQL at all, you could generate API calls to create the table.

So we now have a rather flexible system in place, with the code implementing it being increasingly simple and possible to be “obviously correct”.

Things that easily fall out of this work that people have written about:
– CREATE TABLE LIKE with ENGINE clause
http://krow.livejournal.com/671235.html
– table_raw_reader – looking at the raw representation of table metadata
http://www.flamingspork.com/blog/2009/10/01/table_raw_reader-reading-the-table-proto-from-disk-and-examining-everything/
– Table discovery
http://www.flamingspork.com/blog/2009/07/29/table-discovery-for-drizzle-take-2-now-merged/

Some more info:
http://krow.livejournal.com/642329.html